Reconstructing an image of a scene captured using a compressed sensing device

ABSTRACT

A method for reconstructing an image of a scene captured using a compressed sensing device. A mask is received which identifies at least one region of interest in an image of a scene. Measurements are then obtained of the scene using a compressed sensing device comprising, at least in part, a spatial light modulator configuring a plurality of spatial patterns according to a set of basis functions each having a different spatial resolution. A spatial resolution is adaptively modified according to the mask. Each pattern focuses incoming light of the scene onto a detector which samples sequential measurements of light. These measurements comprise a sequence of projection coefficients corresponding to the scene. Thereafter, an appearance of the scene is reconstructed utilizing a compressed sensing framework which reconstructs the image from the sequence of projection coefficients.

TECHNICAL FIELD

The present invention is directed to systems and methods which use acompressed sensing framework to process an image of a scene capturedusing a compressed sensing device such that when the image isreconstructed pixels associated with one or more regions of interest inthat scene are rendered at a higher quality relative to other pixels inthat image.

BACKGROUND

Adaptive-quality image and video compression is a well-known art wherebyvariable amounts of bits are allocated to different spatial and/ortemporal portions of the data to be compressed depending on the variablequality requirements of the particular application. Traditionalapproaches take as input uncompressed images or videos of a scene,determine the locations of regions of interest (based, for example, onvisual saliency or on the requirements of the particular application,for example, teleconferencing) in the scene, and re-compress the imageor video more efficiently (from a perceptual or application standpoint)by allocating larger amounts of bits to the regions of interest. Thedisadvantage of these traditional approaches is that they are wastefulsince the original data is typically already compressed (for example, atacquisition), which requires performing decompression and adaptivere-compression. Compressive sensing technologies, on the other hand, arecapable of performing image and/or video acquisition and compressionsimultaneously. Compressive sensing can be beneficial because it reducesthe number of samples required to spatially and/or temporallyreconstruct a given scene, thus enabling the use of inexpensive sensorswith reduced spatial and/or temporal resolution in applications wherecomplex sensors are otherwise used, while maintaining the quality of thereconstructed scene. To date, however, compressive sensing techniqueswith adaptive quality capabilities have not been proposed. What isdesirable therefore are methods that can simultaneously offer thebenefits provided by compressive sensing while at the same time enablingadaptive quality scene reconstruction. This is of particular interest inapplications where the video camera is a multi-spectral or hyperspectralimaging system where the spatial sensor is expensive to manufacture.

Accordingly, what is needed in this art are increasingly sophisticatedsystems and methods which use a compressed sensing framework to processan image of a scene captured using a compressed sensing device such thatwhen the image is reconstructed pixels associated with one or moreregions of interest in that scene are rendered at a higher qualityrelative to other pixels in that image.

BRIEF SUMMARY

What is disclosed is a system and method for adaptive-resolution scenereconstruction wherein a region of interest within a scene can berendered at higher quality relative to the rest of that scene. Thepresent method performs an adaptive compression simultaneously withimage acquisition to increase image processing performance whilemaintaining the features and advantages of a compressive sensing system.

In one embodiment, the present method for reconstructing an image of ascene captured using a compressed sensing device involves the following.A mask is received wherein pixels of an image of a scene associated withone or more regions of interest are marked as being ON and pixels notassociated with the region of interest are marked as being OFF.Measurements are then obtained of the scene using a compressed sensingdevice comprising, at least in part, a spatial light modulatorconfiguring a plurality of spatial patterns according to a set of basisfunctions each having spatially varying spatial resolution. It should beunderstood that the basis functions all have the same resolution at agiven spatial location (that is, for given pixel coordinates,) but canhave varying spatial resolution (that is, the resolution can varydepending on the location within the basis function.) A spatialresolution of the basis functions is adaptively modified according toattributes of the mask. For example, when the pixels on the upper halfof the mask associated with the region of interest are ON, the basisfunctions can have twice the resolution in the upper half of the imagerelative to the lower half, in which case, all the basis functions wouldhave varying spatial resolution. In more general embodiments, the maskhas more than two values and different regions of interest arereconstructed with different spatial resolutions. In one extreme case,areas of the scene that are outside the regions of interest are notreconstructed at all, or, equivalently, have zero spatial resolution, sothat targeted region of interest reconstruction is achieved. Eachpattern configured by the spatial light modulator focuses approximatelyhalf of the incoming light of the scene onto a detector which samplessequential measurements of light focused thereon by the pattern. Each ofthe measurements comprises an inner product result y_(m)=<x,φ_(m)>,where x[ ] denotes an N-dimension vector representing the N-pixelsampled version of the scene, with possibly varying resolution andφ_(m)[ ] denotes the m^(th) incoherent basis function used for sampling.A series of measurements comprise a sequence of projection coefficientscorresponding to the inner product between the scene and the differentbasis functions. Thereafter, an appearance of the scene is reconstructedutilizing a compressed sensing framework which reconstructs the imagefrom the sequence of projection coefficients after M inner products havebeen sampled, where M is smaller than N. The reconstruction is such thatpixels associated with the identified region(s) of interest have ahigher image quality when rendered relative to other pixels of theimage.

Many features and advantages of the above-described system and methodwill become readily apparent from the following detailed description andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1A shows an image of a human hand;

FIG. 1B shows an example mask corresponding to the vascular pattern ofthe hand of FIG. 1A;

FIG. 2 shows an example Digital Micromirror Device (DMD) pixel in boththe ON and OFF states;

FIG. 3 shows three example Transmissive Liquid Crystal (TLC)s comprisinga rectangular two-dimensional array of pixel elements configured todisplay alternating strips of modulated light in differing widths;

FIG. 4 illustrates one example embodiment of a compressed sensing devicewherein incoming light of an image of a scene is directed onto a spatiallight modulator which is configured to a sequence of DMD patterns;

FIG. 5 illustrates one example embodiment of the present method forreconstructing an image of a scene captured using a compressed sensingdevice;

FIG. 6A shows an example image of a scene with an identified region ofinterest around the face, and FIG. 6B shows a basis function used in oneexample embodiment of the present methods having higher spatialresolution on the identified region of interest; and

FIG. 7 shows one example embodiment of the present system for performingmeasurements of a regions of interest and for performing scenereconstruction in accordance with the methods disclosed and discussedwith respect to the flow diagram of FIG. 5.

DETAILED DESCRIPTION

What is disclosed is a system and method for adaptive-resolution scenereconstruction wherein a region of interest within a scene can berendered at higher quality relative to the rest of that scene. Adaptivecompression is performed simultaneously with image acquisition toincrease image processing performance and throughput.

NON-LIMITING DEFINITIONS

An “image” of a scene, as is generally understood, comprises a twodimensional grid of pixels with each pixel having an associated locationalong that grid, and an intensity value as detected by imaging elementsof a photodetector.

A “region of interest” of a scene is an identified portion of that scenewhich is intended to be reconstructed using the teachings disclosedherein. What defines a particular area of interest will largely dependon the application where the present invention finds its intended uses.For example, for use in healthcare systems, the region of interest maybe a localized area near vascular pathways of a patient's hand or face.In other contexts, the region of interest may be a patient's neck,thoracic region, a foot or leg, etc. The region of interest may beidentified using a variety of techniques known in the arts including:pixel classification, object identification, facial recognition, color,texture, spatial features, spectral information, pattern recognition,and a user input.

A “mask” identifies at least one region of interest in an image of ascene. In one embodiment, pixels associated with the identified regionof interest are identified in the mask as being ‘active’ and pixelswhich are outside the region of interest are ‘inactive’. FIG. 1B showsan example mask wherein pixel locations that correspond to a region ofinterest, i.e., the vascular pattern of the hand of FIG. 1A, areidentified as being active and pixels that do not correspond to thevascular pattern are identified as being inactive. The mask may havemore than two states.

A “photodetector” or simply “detector” is a device which measures amagnitude of an intensity of the modulated light across pixels in apattern configured by a spatial light modulator. In various embodimentsof the compressed sensing device hereof, the photodetector can be assingle (diode) detector or a multi-diode detector and may furthercomprise an amplifier and an analog-to-digital converter.

A “Spatial Light Modulator (SLM)” is a device in the compressed sensingdevice positioned along an optical axis where a camera's focal planearray would typically be located. The SLM is controllable such that itcan configure a user-specified pattern wherein incoming light isfocused, according to that pattern, onto a photodetector. The SLM can beany of: a Digital Micromirror Device, a Transmissive Liquid Crystal, andreflective Liquid Crystal on Silicon, as are generally understood.

“Digital Micromirror Device (DMD)” is an optical semiconductor whichhas, on its surface, imaging elements comprising microscopicopto-mechanical mirrors arrayed on a two-dimensional grid. Each mirrorin the array is referred to as a DMD pixel. The microscopic mirrors areelectronically controllable and thus modulate incoming light by togglinga reflectivity thereof by individually tilting (or rotating) the mirrorsin one direction or another to achieve an ON/OFF state. In the ON state,light is reflected in a desired direction, typically through a lens oronto a photodetector or both. In the OFF state, the light is directedelsewhere. FIG. 2 shows an example DMD pixel in both the ON and OFFstates. By convention, the positive (+) state is ON and the negative (−)state is OFF. The two states are opposite, i.e., if one element is ‘1’then the other is ‘0’, and vice versa. DMDs are available from a varietyof vendors. One vendor which offers a wide array of opticalsemiconductors is Texas Instruments, Inc. of Dallas, Tex. USA. Thereader is directed to: “Emerging Digital Micromirror Device (DMD)Applications”, by Dana Dudley, Walter Duncan, and John Slaughter,Society of Photo-Optical Instrumentation Engineers (SPIE), Vol. 4985,(2003), which is incorporated herein in its entirety by reference.

A “Transmissive Liquid Crystal (TLC)” also referred to as a “LiquidCrystal Modulator (LCM)”, is a programmable array of liquid crystalelements. Each liquid crystal element in the array is a pixel. Theliquid crystal elements are individually electronically controllable andthus the TLC modulates incoming light by toggling a transparency of eachTLC pixel to achieve an ON/OFF state. By convention, in the ON state,the liquid crystal element is transparent so light passes therethrough.In the OFF state, the liquid crystal element is opaque so light does notpass therethrough. FIG. 3 shows three example TLCs comprising arectangular two-dimensional array of displays wherein each of the TLCpixels have been programmed to form strips of differing widths. Thedarker areas indicate TLC pixels configured to not let light passtherethrough. TLCs are desirable in many applications because of theirfast switching times and a high degree of usability over a broad rangeof visible to infrared wavelength bands. TLCs are available from vendorsin various streams of commerce. One vendor is Jenoptic AG which offersTLCs configured as one and two-dimensional liquid crystal arrays.Jenoptic AG is presently listed on the Frankfurt Stock Exchange. Anothervendor of spatial light modulation devices which offers a line of liquidcrystal on silicon, reflective spatial light modulators is BoulderNonlinear Systems, Inc. of Lafayette, Colo. USA. Various aspects ofspatial light modulators are discussed in the paper entitled: “AdvancesIn Liquid Crystal spatial light modulators”, by Kipp Bauchert, SteveSerati and Alex Furman, Society of Photo-optical InstrumentationEngineers (SPIE), Vol. 4734, (2002), which is incorporated herein in itsentirety by reference.

A “reflective Liquid Crystal on Silicon (LCoS)” refers to amicro-projection or micro-display technology which uses liquid crystalsinstead of individual mirrors. In LCoS, liquid crystals are applieddirectly to the surface of a silicon chip coated with an aluminizedlayer with some type of passivation layer, which is highly reflective.LCoS technology is preferable in many applications because it canproduce higher resolution and higher contrast images than standardliquid crystal technologies.

A “spatially reconstructed scene” refers an image reconstructed frommeasurements obtained by the camera's photodetector. Methods for spatialscene reconstruction from measurements are disclosed in several of theabove-incorporated references. Measurements obtained therefrom are usedto generate a spatially reconstructed image.

Introduction to Compressive Imaging

“Compressive imaging” is an imaging technique which holds a distinctadvantage in detector-noise-limited measurement fidelity (SNR) overconventional camera systems because the total number of photons can bemeasured using a fewer number of detectors. Rather than spatiallysampling an image by collecting the individual pixel data, compressiveimaging measures linear projections of the scene. The resultingprojections are then processed for diverse applications. A relativelylarge number of candidate linear projections such as: wavelets,principal components, Hadamard, discrete-cosine, and pseudo-randomprojections, have been studied in the context of compressive imaging.Linear and nonlinear reconstruction methods have also been exploredincluding linear minimum mean square error using large training setsincluding a variety of nonlinear reconstruction methods which are basedon the compressed sensing theory. See: “Robust Uncertainty Principles:Exact Signal Reconstruction From Highly Incomplete FrequencyInformation”, E. Candès, J. Romberg, and T. Tao, IEEE Trans. InformationTheory, No. 52, pp. 489-509, (2006), and, “Compressed Sensing”, D. L.Donoho, IEEE Trans. Information Theory, No. 52, pp. 1289-1306 (2006),both of which are incorporated herein in their entirety by reference.

“Compressed sensing” is a relatively new area in the signal processingarts where one measures a small number of non-adaptive linearcombinations of a signal. These measurements are usually much smallerthan the number of samples that define the signal. From the smallnumbers of measurements, the signal is reconstructed by a non-linearprocess which aims to reduce the overall complexity required by a largevariety of measurement systems by introducing signal compression intothe measurement process. Essentially, the theory behind compressedsensing is that sparse signal statistics can be recovered from a smallnumber of linear measurements. More generally stated, compressed sensingis any measurement process in which the total number of measurements issmaller than the dimensionality of the signals of interest beingexplored. The sparse nature of most signals of interest allowshigh-fidelity reconstructions to be made using a compressed sensingapproach. The reader is directed to the following textbooks on thissubject: “Compressed Sensing: Theory and Applications”, CambridgeUniversity Press; 1^(st) Ed. (2012), ISBN-13:978-1107005587 and “SparseRepresentations and Compressive Sensing for Imaging and Vision”,Springer (2013), ISBN-13: 978-1461463801, which are incorporated hereinin their entirety by reference. The field of digital imaging is a goodcandidate for compressed sensing due to the large amount of raw datathat can be acquired by arrays of imaging sensors. Compared toconventional imaging, compressive imaging takes advantage of themathematical framework and theorems of compressed sensing to provideimproved performance with reduced complexity. This reduced complexitycan be of particular importance, for example, in mid-wave infrared(MWIR) image systems where detector arrays are less developed and moreexpensive than photodetector array technology used in visible imaging.Compressive imaging provides greater insight into how a high resolutionimage can be inferred from a small number of measurements.

A “compressed sensing device” is a single-pixel camera architecturewherein spatial measurements taken by the focal plane array of aconventional camera architecture are effectively replaced by a series oftemporal measurements taken by a single (diode) detector or amulti-diode detector. In a compressed sensing device, incoming light ofan image is focused onto a spatial light modulator (SLM) such as aDigital Micromirror Device (DMD). The DMD is an array ofelectronically-controllable micro-mirrors which can be individuallyconfigured to tilt in one direction or the other to achieve a desiredpattern. When tilted to “select” light, the mirror reflects incominglight onto the detector. When tilted to “reject” light, the mirrorreflects light away from the detector. As prescribed by compressivesensing theory, each DMD pattern is configured to select approximatelyone-half of the incoming light of the image onto the detector. Duringimage acquisition, a series of unique patterns are sequentially providedto the DMD and a series of measurements are obtained. Light energy isconcentrated by that DMD mirror pattern onto the diode where the photonsof the image are converted to an electrical signal. Each signal,produced as a result of each measurement, is a function of a specificpattern. By rapidly changing the DMD patterns and obtaining measurementstherefrom, a time-series signal is obtained. Utilizing a compressedsensing framework, an image reconstruction algorithm reconstructs theoriginal image from the generated time-series measurement data withknowledge of the temporal sequence of patterns. FIG. 4 illustrates oneexample embodiment compressed sensing device wherein incoming light ofan image 402 of a scene is directed onto a spatial light modulator 402which is configured to a sequence of DMD patterns, collectively at 404.The configured pattern of reflected incoming light is directed to acondenser 405 which focuses the pattern of light onto a photodetectorwhich senses a magnitude of the detected light. A signal is output bythe photodetector which is directed to an A/D converter 407 whichoutputs a signal 408 corresponding to the sensed image. The generatedsignal 408 is provided to an image reconstruction module 409 which,using a compressed sensing framework, generates a reconstructed image410 as output.

A “compressed sensing framework” is a signal processing technique forreconstructing a signal with solutions found by taking advantage of thesignal's sparseness or compressibility in some domain, thereby enablingthe entire signal to be generated from relatively few measurements. Anunderdetermined linear system has more unknowns than equations andgenerally has an infinite number of solutions. In order to choose aproper solution, constraints are applied. Because many signals aresparse, i.e., they contain many coefficients close to or equal to zerowhen represented in some domain, the additional constraint of scarcityallows only those solutions with a small number of non-zerocoefficients. Not all underdetermined systems have a sparse solution.However, if there is a unique sparse representation to thatunderdetermined linear system then a compressed sensing frameworkenables a recovery of that solution. The compressive sensing frameworkhereof utilizes basis functions on which the scene is projected via thecomputation of inner products: the resolution of the reconstructed scenewill be the same as that of the basis functions: the reconstructed scenewill have higher spatial resolution at locations associated with regionsof interest, as indicated by the mask, which correspond to areas ofhigher spatial resolution in the basis functions. In one extreme case,areas of the scene that are outside the regions of interest are notreconstructed at all, or, equivalently, have zero spatial resolution, sothat targeted region of interest reconstruction is achieved.Specifically, let x[ ] denote the N-pixel sampled version of the imagescene and φ_(m)[ ] is the m^(th) incoherent basis function used forsampling. Each measurement performed by the sensing stage corresponds tothe inner product y_(m)=<x,φ_(m)>. The sampling basis functions aretypically generated via the use of pseudorandom number generators (e.g.Gaussian, Bernoulli, etc.) that produce patterns with close to 50% fillfactor. By making the basis functions pseudorandom, the N-pixel sampledscene image x[ ] can typically be reconstructed with significantly fewersamples than those dictated by the Nyquist sampling theorem (i.e., theimage can be reconstructed after M inner products w where M is smallerthan N. Traditional compressive sensing techniques implement basisfunctions that lie on a uniform lattice. This necessarily implies thatthe basis functions themselves have a fixed spatial resolution.According to the teachings hereof, the basis functions exist on morethan one lattice, each with a different spatial resolution. FIG. 6Ashows an example image of a scene with an identified region of interestaround the face, and FIG. 6B shows a basis function with variablespatial resolution according to the teachings herein: the basis functionhas higher pixel density on the region identified to be of interest.This results in a reconstructed scene whose resolution can varydepending on spatial location. Since it is the objective hereof torecover regions of interest within the scene with a high quality, theresolution of the lattice will be larger on locations associated withthe identified region of interest (ROI). The quality difference betweenROI and non-ROI areas will depend on the application and/or onrate-distortion requirements.

Flow Diagram of One Embodiment

Reference is now being made to the flow diagram of FIG. 5 whichillustrates one example embodiment of the present method forreconstructing an image of a scene captured using a compressed sensingdevice in accordance with the teachings hereof. Flow processing beginsat step 500 and immediately proceeds to step 502.

At step 502, receive a mask which identifies at least one region ofinterest in an image of a scene. One example mask is shown and describedwith respect to FIGS. 1A-B. The mask effectively identifies one or moreregions of interest in a scene which are desired to be enhanced usingthe teachings hereof.

At step 504, use the mask to adaptively modify a spatial resolution of aset of basis functions.

At step 506, use a compressed sensing device to obtain a series ofmeasurements comprising a sequence of projection coefficients thatcorrespond to the scene being imaged. Each of the measurements comprisesan inner product result.

At step 508, reconstruct an appearance of the scene utilizing acompressed sensing framework which reconstructs the image from thesequence of projection coefficients after M inner products have beensampled. The reconstruction is such that pixels associated with theregion of interest, as identified by the mask, have a higher imagequality when rendered relative to other pixels of the image. Thereafter,in this embodiment, further processing stops.

It should be appreciated that the flow diagrams hereof are illustrative.One or more of the operations illustrated in the flow diagrams may beperformed in a differing order. Other operations may be added, modified,enhanced, or consolidated. Variations thereof are intended to fallwithin the scope of the appended claims.

Block Diagram of a System Architecture

Reference is now being made to FIG. 7 shows one example embodiment ofthe present system for performing measurements of a region of interestand for performing scene reconstruction in accordance with the methodsdisclosed and discussed with respect to the flow diagram of FIG. 5.

In the system of FIG. 7, incoming light (collectively at 701) enters thecompressed sensing device 700 through a light-gathering aperture 702 andinto a spatial light modulator (SLM) 703. Theelectronically-configurable two-dimensional array of selectablyadjustable imaging elements comprising the light modulation device 703modulates incoming light 701 to modulate a pattern 704 onto Detector 705which measures a magnitude of an intensity of the modulated pattern 704.Mask Module 708 receives a mask using, for instance, communication port709 which may comprise a wired or wireless connection, and provides themask to a Controller 707 shown comprising at least a processor andmemory. Controller 707 facilitates a configuration of theelectronically-controllable spatial light modulator 703 to modulateincoming light according to attributes of the mask. Each pattern of thespatial light modulator 703 focuses a portion of the incoming light 701onto a sensing element of Detector 705. Detector 705 outputs ameasurement 706. The measurements 706 may be provided as output via port715 which may comprise, for example, a USB port. Measurements obtainedby the Detector are communicated to Image Reconstruction Module 713wherein a spatial appearance of the scene is reconstructed using acompressed sensing framework. The measurements 706 and the reconstructedimage 714 are communicated to storage device 716 and/or provided asoutput to an external device such as, for example, workstation 720. Anyof the values, data, measurements, and results of any of the modules andprocessing units of the system 700 may be obtained or retrieved viacommunications bus 717.

Shown in communication with the system of FIG. 7 is a workstation 720.In this embodiment, the workstation is shown comprising a monitor 721, akeyboard 722 and a mouse 723, collectively a user interface. Theworkstation also has a storage device 724 and a computer readable media725. Information stored to media 725 can be retrieved using, forexample, a CD-ROM drive. Workstation 720 is also placed in communicationwith one or more remote devices over network 726 using, for example, anetwork card internal to the workstation. Using the user interface ofthe workstation, a user thereof may change or control the functionalityof any of the modules and processing units comprising the compressedsensing system 700. Images can be displayed on the display device 721wherein images may be corrected and cropped. Masks can be generatedusing the workstation and communicated to the mask module 708.Measurements and values generated by the system 700 may be displayed onthe display device 721. Intensity values obtained by Detector 705 may bemodified by a user of the workstation. The pattern of modulated lightmay further be communicated to the workstation and displayed on thedisplay device wherein a user can selectively identify a region ofinterest using, for example, a mouse to make a selection by placing arubber-band box around one or more areas in an image. The workstationmay further be used to identify one or more localized areas of a givenregion of interest. The identified localized area of interest can becommunicated to the mask module, depending on the embodiment. Anoperator of the workstation may modify the results generated by any ofthe modules or processing units comprising the system of FIG. 7 asneeded and/or re-direct the modified results back to the same ordifferent modules for further processing or re-processing. It should beappreciated that the workstation has an operating system and otherspecialized software configured to display a variety of numeric values,text, scroll bars, pull-down menus with user selectable options, and thelike, for entering, selecting, or modifying information displayed on thedisplay device. In other embodiments, the generated results are providedto a server over the network and communicated to a user/operator suchas, a physician, nurse, technician, cardiac specialist, to name a few.

The system of FIG. 7 may further comprise a registration module (notshown) to effectuate a pixel-wise registration between pixels identifiedby the mask and the image of the reconstructed scene. Such aregistration module would generate a registered mask depending on theimplementation. The system may further comprise means for togglingbetween multiple spectral bands in a multi-band operation.

Various modules may designate one or more components which may, in turn,comprise software and/or hardware designed to perform the intendedfunction. A plurality of modules may collectively perform a singlefunction. Each module may have a specialized processor capable ofexecuting machine readable program instructions. A module may comprise asingle piece of hardware such as an ASIC, electronic circuit, or specialpurpose processor. A plurality of modules may be executed by either asingle special purpose computer system or a plurality of special purposecomputer systems in parallel. Modules may include software/hardwarewhich may further comprise an operating system, drivers, controllers,and other apparatuses some or all of which may be connected via anetwork.

One or more aspects of the systems and methods described herein areintended to be incorporated in an article of manufacture which may beshipped, sold, leased, or otherwise provided separately either alone oras part of a product suite. The above-disclosed features and functionsor alternatives thereof, may be combined into other systems orapplications. Presently unforeseen or unanticipated alternatives,modifications, variations, or improvements may become apparent and/orsubsequently made by those skilled in the art and, further, may bedesirably combined into other different systems or applications. Changesto the above-described embodiments may be made without departing fromthe spirit and scope of the invention. The teachings of any printedpublications including patents and patent applications, are eachseparately hereby incorporated by reference in their entirety.

What is claimed is:
 1. A method for reconstructing an image of a scenecaptured using a compressed sensing device, the method comprising:obtaining measurements of a scene using a spatial light modulatorconfiguring a plurality of spatial patterns according to a set of basisfunctions, each entry of the set of basis functions generated from apseudorandom number generator and each basis function in the set ofbasis functions existing on more than one lattice, each basis functionhaving a spatial resolution that is variable, the spatial resolution ofsaid set of basis functions being adaptively modified according toattributes of a mask which identifies at least one region of interest inan image of said scene, each pattern of said spatial light modulatorfocuses a portion of the incoming light of said scene onto at least onedetector which samples sequential measurements of light focused thereon,said measurements comprising a sequence of projection coefficientscorresponding to said scene and said set of basis functions; andreconstructing an appearance of said scene utilizing a compressedsensing framework which reconstructs an image from said sequence ofprojection coefficients such that pixels associated with said at leastone region of interest as identified by the mask have a higher spatialresolution relative to other pixels of said image.
 2. The method ofclaim 1, wherein said spatial light modulator comprises one of: adigital micromirror device comprising a two dimensional array ofelectronically controllable micro-mirrors, a transmissive liquidcrystal, and a reflective liquid crystal on silicon.
 3. The method ofclaim 1, wherein said detector comprises at least one diode, anamplifier, and an analog-to-digital converter.
 4. The method of claim 1,wherein said regions of interest are identified in said image using anyof: pixel classification, object identification, facial recognition,color, texture, spatial features, spectral information, patternrecognition, and a user input.
 5. The method of claim 1, wherein saidbasis functions are generated via the pseudorandom number generatorcapable of producing patterns with close to 50% fill.
 6. The method ofclaim 1, wherein said detector is configured to detect any of: aninfrared wavelength band, and a visible wavelength band.
 7. The methodof claim 1, wherein each of said measurements comprises an inner productresult y_(m)=<x,φ_(m)>, where x denotes an N-dimension vectorrepresenting the N-pixel sampled version of said scene and φ_(m) denotesthe m^(th) incoherent basis function used for sampling, a series of saidmeasurements comprising a sequence of projection coefficientscorresponding to said scene.
 8. The method of claim 7, whereinreconstructing an appearance of said scene utilizes a compressed sensingframework which reconstructs said image from said sequence of projectioncoefficients after M inner products have been sampled, where M issmaller than N.
 9. A method comprising: receiving a mask that identifiesat least one region of interest in an image of a scene; obtainingmeasurements of the scene using a spatial light modulator thatconfigures a plurality of spatial patterns according to a set of basisfunctions, each entry of the set of basis functions generated from apseudorandom number generator and each basis function in the set ofbasis functions existing on more than one lattice, with each basisfunction having a different spatial resolution; wherein said obtainingmeasurements further comprises: (i) using the mask to adaptively modifythe spatial resolution of at least one basis function in the set ofbasis functions; and (ii) sampling sequential measurements of incominglight of the scene focused onto a detector using a plurality of thespatial patterns; each sequential measurement having a sequence ofprojection coefficients corresponding to the scene and the set of basisfunctions; and reconstructing the image of the scene from the sequenceof projection coefficients, where the at least one region of interest inthe reconstructed image of the scene has a higher resolution thenregions outside the at least one region of interest.
 10. The method ofclaim 9, wherein said spatial light modulator comprises one of: adigital micromirror device comprising a two dimensional array ofelectronically controllable micro-mirrors, a transmissive liquidcrystal, and a reflective liquid crystal on silicon.
 11. The method ofclaim 9, wherein said detector comprises at least one diode, anamplifier, and an analog-to-digital converter.
 12. The method of claim9, wherein said regions of interest are identified in said image usingany of: pixel classification, object identification, facial recognition,color, texture, spatial features, spectral information, patternrecognition, and a user input.
 13. The method of claim 9, wherein saidbasis functions are generated via the pseudorandom number generatorcapable of producing patterns with close to 50% fill.
 14. The method ofclaim 9, wherein said detector is configured to detect any of: aninfrared wavelength band, and a visible wavelength band.
 15. The methodof claim 9, wherein each of said measurements comprises an inner productresult y_(m)=<x,φ_(m)>, where x denotes an N-dimension vectorrepresenting the N-pixel sampled version of said scene and φ_(m) denotesthe m^(th) incoherent basis function used for sampling, a series of saidmeasurements comprising a sequence of projection coefficientscorresponding to said scene.
 16. The method of claim 15, whereinreconstructing the image of the scene utilizes a compressed sensingframework which reconstructs said image from said sequence of projectioncoefficients after M inner products have been sampled, where M issmaller than N.